This disclosure relates generally to the art of machine diagnostics and repair and also to the design of diagnostic aids such as fault trees. More particularly, the disclosure relates to a method and system of assigning priorities to individual test modules in a fault tree based on statistical feedback from persons using the fault tree in the field. A benefit of the disclosure is that it allows the fault tree to be revised, based on the statistical feedback, and allows future users of the revised fault tree to correctly diagnose a problem with the machine more quickly.
Generally speaking, a fault tree is a flow chart in the form of a series of test steps or procedures that a technician uses to diagnose the cause of a malfunction or other condition in a machine. The machine could be any kind of machine, for example a copy machine, a printing press, a refrigerator, a medical diagnostic instrument, a component or subsystem of an aircraft, or an automobile engine. The fault tree is typically prepared for service technicians by the machine's manufacturer, but also can be authored in-house by the company owning or using the machine. Fault trees are typically published in repair or service manuals for the machine. They may also be available on-line and accessed by a technician over the Internet using a computer. The fault trees may also be stored on mobile, computer-based machine diagnostic systems, such as, for example, the Modular Diagnostic and Information System (MODIS) for vehicle repair, available from Snap-On Technologies Inc.
Fault trees are typically prepared by engineers and designers employed by the machine manufacturer, and printed and distributed at the time the machine is first manufactured and sold commercially for the benefit of field service technicians. The fault trees typically represent the machine's designer's best estimate of the optimum sequence of test procedures to arrive at a diagnosis of machine fault or error, with a minimum of trial and error. However, the real world experience of technicians in the field sometimes is very different from the predictions and estimations of the machine designers. As such, over the life of the machine the fault trees can become out of date and fail to reflect the real world experience of service technicians in the field. While fault trees are often updated, the updates are based on feedback personally provided to the author, e.g., by phone, email or input from peers.
For example, the machine designer will typically have the first test step in the fault tree calculated to uncover the designer's prediction of the most likely failure or fault given a certain symptom, the second test step to uncover the second most likely fault, etc. However, the technicians in the field may discover, for example, that the fourth test step in the fault tree is more likely to reveal the fault in the machine more than the first or second step, or that the first two steps in the procedure do not reveal the source of the problem most of the time whereas the third through fifth steps are more likely to reveal the source of the problem. Accordingly, in this situation the fault tree is out of step with the experience of the technicians. If the technician follows the fault tree in the order originally specified by the manufacturer, as they are trained to do, they spend valuable time performing diagnostic steps that make no progress towards the diagnosis more often than they should.
This disclosure provides a more automated way of examining how steps in a fault tree are used and how often they result in a correct diagnosis, and using that information to improve the fault trees.